Navigating Ideation Space: Decomposed Conceptual Representations for Positioning Scientific Ideas

TL;DR

This work introduces Ideation Space, a decomposed representation of scientific knowledge into three orthogonal sub-spaces—problem, method, and findings—learned via citation-contextual contrastive training to enable fine-grained conceptual similarity and ideation-transition modeling. It then builds a Hierarchical Sub-Space Retrieval framework and a Decomposed Novelty Assessment algorithm that leverage five databases (three node, two transition) and reasoning-graph aggregation to identify which aspects of an idea are novel. Experimental results show substantial improvements in Recall@K and transition-pattern retrieval, and a moderate correlation with expert novelty judgments, demonstrating a scalable, retrieval-grounded approach to accelerating scientific discovery. The framework offers a promising paradigm for literature review, peer evaluation, and guiding researchers toward more impactful contributions in rapidly expanding research domains.

Abstract

Scientific discovery is a cumulative process and requires new ideas to be situated within an ever-expanding landscape of existing knowledge. An emerging and critical challenge is how to identify conceptually relevant prior work from rapidly growing literature, and assess how a new idea differentiates from existing research. Current embedding approaches typically conflate distinct conceptual aspects into single representations and cannot support fine-grained literature retrieval; meanwhile, LLM-based evaluators are subject to sycophancy biases, failing to provide discriminative novelty assessment. To tackle these challenges, we introduce the Ideation Space, a structured representation that decomposes scientific knowledge into three distinct dimensions, i.e., research problem, methodology, and core findings, each learned through contrastive training. This framework enables principled measurement of conceptual distance between ideas, and modeling of ideation transitions that capture the logical connections within a proposed idea. Building upon this representation, we propose a Hierarchical Sub-Space Retrieval framework for efficient, targeted literature retrieval, and a Decomposed Novelty Assessment algorithm that identifies which aspects of an idea are novel. Extensive experiments demonstrate substantial improvements, where our approach achieves Recall@30 of 0.329 (16.7% over baselines), our ideation transition retrieval reaches Hit Rate@30 of 0.643, and novelty assessment attains 0.37 correlation with expert judgments. In summary, our work provides a promising paradigm for future research on accelerating and evaluating scientific discovery.